The distributed remote sourcecoding (the so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The...
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The distributed remote sourcecoding (the so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for the sum-rate-distortion function under arbitrary distortion measures. When specialized to the case of mean-squared error, it is shown that the bound exactly mirrors a corresponding upper bound, except that the upper bound has the source power (variance), whereas the lower bound has the source entropy power. Bounds exhibiting this pleasing duality of power and entropy power have been well known for direct and centralized sourcecoding since Shannon's work. While the bounds hold generally, their value is most pronounced when interpreted as a function of the number of agents in the CEO problem.
The information-theoretic notion of energy efficiency is studied in the context of various joint source-channel coding problems. The minimum transmission energy E(D) required to communicate a source over a noisy chann...
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The information-theoretic notion of energy efficiency is studied in the context of various joint source-channel coding problems. The minimum transmission energy E(D) required to communicate a source over a noisy channel so that it can be reconstructed within a target distortion D is analyzed. Unlike the traditional joint source-channel coding formalisms, no restrictions are imposed on the number of channel uses per source sample. For single-source memoryless point-to-point channels, E(D) is shown to be equal to the product of the minimum energy per bit E-bmin of the channel and the rate-distortion function R(D) of the source, regardless of whether channel output feedback is available at the transmitter. The primary focus is on Gaussian sources and channels affected by additive white Gaussian noise under quadratic distortion criteria, with or without perfect channel output feedback. In particular, for two correlated Gaussian sources communicated over a Gaussian multiple-access channel, inner and outer bounds on the energy-distortion region are obtained, which coincide in special cases. For symmetric channels, the difference between the upper and lower bounds on energy is shown to be at most a constant even when the lower bound goes to infinity as D -> 0. It is also shown that simple uncoded transmission schemes perform better than the separation-based schemes in many different regimes, both with and without feedback.
Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless...
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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.
The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) du...
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The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) due to low computational capability and limited power supply, achieving recognition accuracy under limited bandwidth and noisy channel of wireless networks is a crucial but challenging task. In this paper, we propose a deep learning-constructed joint transmission-recognition scheme for the IoT devices to effectively transmit data wirelessly to the server for recognition, jointly considering transmission bandwidth, transmission reliability, complexity, and recognition accuracy. Compared with other schemes that may be deployed on the IoT devices, i.e., a scheme based on JPEG compression and two compressed sensing-based schemes, the proposed deep neural network-based scheme has much higher recognition accuracy under various transmission scenarios at all signal-to-noise ratios (SNRs). In particular, the proposed scheme maintains good performance at the very low SNR. Moreover, the complexity of the proposed scheme is low, making it suitable for IoT applications. Finally, a transfer learning-based training method is proposed to effectively mitigate the computing burden and reduce the overhead of online training.
With the purpose of reducing the coding complexity and delay of the separation-based schemes, an analog joint source-channel coding scheme is proposed for transmissions through parallel AWGN channels with side informa...
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ISBN:
(纸本)9781467310680
With the purpose of reducing the coding complexity and delay of the separation-based schemes, an analog joint source-channel coding scheme is proposed for transmissions through parallel AWGN channels with side information at the receiver. This scheme divides the bidimensional source space into a set of hexagons and transmits the relative position of the source vectors inside the corresponding hexagon by using two complimentary analog mappings. The results are satisfactory, specially taking into consideration the low complexity and delay of the proposed scheme.
We consider the problem of tracking, in realtime, an unstable autoregressive (AR) source over a discrete memory-less channel (DMC). We present computable achievable bounds on the optimal tracking error for general DMC...
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We consider the problem of tracking, in realtime, an unstable autoregressive (AR) source over a discrete memory-less channel (DMC). We present computable achievable bounds on the optimal tracking error for general DMCs, and we particularize these bounds to the binary erasure, packet erasure, and binary symmetric channels. The achievable bounds in this paper are proved using a partially separate source quantization and channelcoding architecture. We do not use complete or strict separation in usual Shannon sense: 1) the quantiser's resolution is optimized against the error-correction capabilities of the channel code and the channel code is optimized against an AR Hamming distortion function matched to the source (a weighted Hamming distortion function that provides unequal error protection to different parts of the AR source). The achievability results for general DMCs are proved by combining the AR Hamming distortion function with new realtime (streaming) versions of the random coding union and dependence testing bounds. When applied to erasure channels, these general bounds combine with simple converses to demonstrate that the channel's cutoff rate plays an important role in realtime tracking.
This paper considers the problem of impulse noise mitigation for videos encoded using a SoftCast-based Linear Video coding (LVC) scheme and transmitted using an OFDM scheme over a wideband channel prone to impulse noi...
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ISBN:
(纸本)9781479981311
This paper considers the problem of impulse noise mitigation for videos encoded using a SoftCast-based Linear Video coding (LVC) scheme and transmitted using an OFDM scheme over a wideband channel prone to impulse noise. In the time domain, the impulse noise is modeled as realizations of iid Bernoulli-Gaussian variables. A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation. This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes. Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise. Using a phenomenological model (PM) of the residual noise variance after impulse correction, an algorithm is proposed to evaluate the optimal number of subchannels to provision for impulse noise mitigation. Simulation results show that the PM can accurately predict the number of subchannels to provision and that impulse noise mitigation can significantly improve the decoded video quality compared to a situation where all subchannels are used for data transmission.
Arithmetic codes are being increasingly used in the entropy coding stage in many multimedia transmission applications. Combining channelcoding with arithmetic coding can give implementation and performance advantages...
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ISBN:
(纸本)9783642252051
Arithmetic codes are being increasingly used in the entropy coding stage in many multimedia transmission applications. Combining channelcoding with arithmetic coding can give implementation and performance advantages compared to separate source and channelcoding. In this work, novel improvements are introduced into a technique by Grangetto et al. that uses maximum a posteriori (MAP) estimation for decodingjoint source-channel coding using arithmetic codes. The arithmetic decoder is modified for quicker symbol decoding and error detection by the introduction of a look-ahead technique. and the calculation of the MAP metric is modified for faster error detection. These modifications also result in improved performance compared to the original scheme. Experimental results show an improvement of up to 0.4 dB when using soft-decision decoding and 0.6 dB when using hard-decision decoding.
This paper shows new tight finite-blocklength bounds for the best achievable lossy jointsource-channel code rate, and demonstrates that jointsource-channel code design brings considerable performance advantage over ...
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ISBN:
(纸本)9781467325790
This paper shows new tight finite-blocklength bounds for the best achievable lossy jointsource-channel code rate, and demonstrates that jointsource-channel code design brings considerable performance advantage over a separate one in the non-asymptotic regime. A jointsource-channel code maps a block of k source symbols onto a length-n channel codeword, and the fidelity of reproduction at the receiver end is measured by the probability epsilon that the distortion exceeds a given threshold d. For memoryless sources and channels, it is demonstrated that the parameters of the best jointsource-channel code must satisfy nC - kR(d) approximate to root nV + kV(d)Q(-1) (epsilon), where C and V are the channel capacity and dispersion, respectively;R(d) and V(d) are the source rate-distortion and rate-dispersion functions;and Q is the standard Gaussian complementary cdf.
We consider the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading, which we term the lossy multi-connectivity problem. We assume that only ...
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We consider the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading, which we term the lossy multi-connectivity problem. We assume that only the decoder has access to the channel state information. Motivated by ultra-reliable and low latency communication requirements, we are interested in the finite blocklength performance of the problem, i.e., the minimal excess-distortion probability of transmitting k source symbols over n channel uses. By generalizing non-asymptotic bounds by Kostina and Verdu for the lossy joint source-channel coding problem, we derive non-asymptotic achievability and converse bounds for the lossy multi-connectivity problem. Using these non-asymptotic bounds and under mild conditions on the fading distribution, we derive approximations for the finite blocklength performance in the spirit of second-order asymptotics for any discrete memoryless source under an arbitrary bounded distortion measure. Furthermore, in the achievability part, we analyze the performance of a universal coding scheme by modifying the universal joint source-channel coding scheme by Csiszar and using a generalized minimum distance decoder. Our results demonstrate that the asymptotic notions of outage probability and outage capacity are in fact reasonable criteria even in the finite blocklength regime. Finally, we illustrate our results via numerical examples.
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